Evaluating the Ability to Map the Degree of Informality within a City using a Scalable, Machine Learning Methodology in Nairobi, Kenya

Updatable and scalable maps of urban deprivation are needed to plan, upgrade, and monitor dynamic neighborhood-level changes within developing world cities, especially in Sub-Saharan Africa. Earth Observation data provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images, which often cover only small areas and are cost prohibitive. Additionally, most of the previous work has only focused on distinguishing only between slums and formal areas. Our current work has started to look into moving beyond, slum-non slum dichotomy to look at the degree of deprivation within a city. Therefore, this work focuses on determining if we can expand our slum, non-slum, scalable machine learning approaches to represent this degree of deprivation. This is our first attempt to do this, and model results indicate moderate accuracy, but tend to over predict slum areas, especially in the areas where our on the ground data are captured. Future work should focus on understanding modeling uncertainty, expanding on the ground data locations, and the model inputs to determine our ability to scale up the degree of informality data.